Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring

Klaus Robert Müller; Michael Tangermann; Guido Dornhege; Matthias Krauledat; Gabriel Curio; Benjamin Blankertz

doi:10.1016/j.jneumeth.2007.09.022

Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring

Klaus Robert Müller, Michael Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, Benjamin Blankertz

Research output: Contribution to journal › Article › peer-review

380 Citations (Scopus)

Abstract

Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.

Original language	English
Pages (from-to)	82-90
Number of pages	9
Journal	Journal of Neuroscience Methods
Volume	167
Issue number	1
DOIs	https://doi.org/10.1016/j.jneumeth.2007.09.022
Publication status	Published - 2008 Jan 15

Bibliographical note

Funding Information:
We gratefully acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF), 01IBE01A/B and 01IGQ0414, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778.

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

EEG
Machine learning
Mental state monitoring
Real-time
Sensorimotor rhythms
Single-trial EEG-analysis
α-Rhythm

ASJC Scopus subject areas

General Neuroscience

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10.1016/j.jneumeth.2007.09.022

Cite this

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title = "Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring",

abstract = "Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain-computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain-computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6-8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.",

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note = "Funding Information: We gratefully acknowledge financial support by the Bundesministerium f{\"u}r Bildung und Forschung (BMBF), 01IBE01A/B and 01IGQ0414, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.",

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doi = "10.1016/j.jneumeth.2007.09.022",

language = "English",

volume = "167",

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AU - Dornhege, Guido

AU - Krauledat, Matthias

AU - Curio, Gabriel

AU - Blankertz, Benjamin

N1 - Funding Information: We gratefully acknowledge financial support by the Bundesministerium für Bildung und Forschung (BMBF), 01IBE01A/B and 01IGQ0414, by the Deutsche Forschungsgemeinschaft (DFG), FOR 375/B1, and by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. Copyright: Copyright 2008 Elsevier B.V., All rights reserved.

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KW - Machine learning

KW - Mental state monitoring

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KW - Sensorimotor rhythms

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Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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